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[Diffusion]: Diffusion Ring Attention support (#273)

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Signed-off-by: mxuax <mxuax@connect.ust.hk>
Signed-off-by: Hongsheng Liu <liuhongsheng4@huawei.com>
Co-authored-by: Jiangyun Zhu <riverclouds.zhu@qq.com>
Co-authored-by: Hongsheng Liu <liuhongsheng4@huawei.com>
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XU MingshiJiangyun ZhuHongsheng Liu
 authored and GitHub committed 2025-12-31 14:26:30 +00:00
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docs/user_guide/diffusion_acceleration.md
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@@ -17,6 +17,7 @@ Both methods can provide significant speedups (typically **1.5x-2.0x**) while ma
vLLM-Omni also supports the sequence parallelism (SP) for the diffusion model, that includes:
1. [Ulysses-SP](acceleration/parallelism_acceleration.md#ulysses-sp) - splits the input along the sequence dimension and uses all-to-all communication to allow each device to compute only a subset of attention heads.
2. [Ring-Attention](acceleration/parallelism_acceleration.md#ring-attention) - splits the input along the sequence dimension and uses ring-based P2P communication to accumulate attention results, keeping the sequence dimension sharded.
## Quick Comparison
@@ -33,6 +34,14 @@ The following table shows which models are currently supported by each accelerat
### ImageGen
<<<<<<< HEAD
| Model | Model Identifier | TeaCache | Cache-DiT | Ulysses-SP | Ring-Attention |
|-------|-----------------|----------|-----------|-----------|----------------|
| **Qwen-Image** | `Qwen/Qwen-Image` | ✅ | ✅ | ✅ | ✅ |
| **Z-Image** | `Tongyi-MAI/Z-Image-Turbo` | ❌ | ✅ |❌ | ❌ |
| **Qwen-Image-Edit** | `Qwen/Qwen-Image-Edit` | ✅ | ✅ |✅ | - |
| **Qwen-Image-Edit-2509** | `Qwen/Qwen-Image-Edit-2509` | ❌ | ✅ |✅ | - |
=======
| Model | Model Identifier | TeaCache | Cache-DiT | Ulysses-SP |
|-------|------------------|----------|-----------|-----------|
| **LongCat-Image** | `meituan-longcat/LongCat-Image` | ❌ | ✅ | ❌ |
@@ -151,6 +160,22 @@ outputs = omni.generate(prompt="turn this cat to a dog",
pil_image=input_image, num_inference_steps=50)
```
### Using Ring-Attention
Run text-to-image:
```python
from vllm_omni import Omni
from vllm_omni.diffusion.data import DiffusionParallelConfig
ring_degree = 2
omni = Omni(
model="Qwen/Qwen-Image",
parallel_config=DiffusionParallelConfig(ring_degree=2)
)
outputs = omni.generate(prompt="A cat sitting on a windowsill", num_inference_steps=50, width=2048, height=2048)
```
## Documentation
For detailed information on each acceleration method: